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DecisionX Founder & CEO Ranjan Kumar joined host Antara Gupta on AIM's Point Break, alongside Heather Dawe (UST) and Anil Agarwal (InCruiter), to debate why Satya Nadella, Jensen Huang, Marc Benioff and Alex Karp are suddenly warning enterprises about the very technology they champion.
Four of AI's most influential builders, Satya Nadella, Jensen Huang, Marc Benioff and Alex Karp, have all recently converged on the same warning: enterprises may be quietly handing over their most valuable institutional knowledge through the prompts, corrections and workflows they generate while using AI tools. AIM's Point Break brought together three practitioners to debate whether that convergence is genuine concern or strategic positioning, and what enterprises should actually do about it.
Ranjan framed the debate around a simple idea: the last two years of AI adoption digitized information and automation, but judgement, the accumulated, contextual decision-making of experienced people inside an organisation, remains the one asset that hasn't been captured yet, and it's the one enterprises most need to protect. He walked through how DecisionX approaches this as an engineering problem rather than a model problem: building a unified decision ontology that lives entirely on the customer's own infrastructure, so an enterprise can draw on frontier models for reasoning without ever exposing its underlying data or decision history to them.
Asked directly where enterprise sovereignty actually lives, in model weights or in the causal graph, his answer was unambiguous: the ontology itself. He also pushed back on the idea that this is only a big-enterprise problem, pointing to high-stakes, high-governance industries like pharma as where this distinction matters most.
"We never store customer data or send it to an AI model. Instead, we build a context layer that stays inside the customer's own infrastructure, so their decisions and know-how remain their IP, not ours, and not the model's."
Ranjan Kumar, Founder & CEO, DecisionXDecisionX puts Decision AI into practice by continuously monitoring signals, structuring context, reasoning across hypotheses, and surfacing the next best action within a single system.